Traditional Vs Cyber-bullying: Associations with Cognitive
Mechanisms and Interpersonal Variables in European and Asian
Sample
By
Joseph O’ Dwyer
Submitted in partial fulfilment of the requirements of the Bachelor of Arts
degree (Psychology Specialization) at DBS School of Arts, Dublin.
Supervisor: Margaret Walsh
Head of Department: Dr S. Eccles
March 2012
Department of Psychology
DBS School of Arts
Traditional Vs Cyber-Bullying 2
Table of Contents
Table of Contents……………………………………………………………………..……….2
List of Figures………………………………………………………………………..………..5
List of Tables & Appendices……………………………………………………..……………5
Acknowledgements……………………………………………………………….…………...7
Abstract…………………………………………………………………………….………….8
Introduction……………………………………………………………………………………9
History of Bullying………………………………………………………….…………9
Cyber-Bullying………………………………………………………………...……..11
Types of Bullying……………………………………………………………...……..12
Incidence Rates of Universal Problem Bullying & Cyber-bullying………………....12
Gender Differences in Bullying……………………………………………………...13
Overlaping of Traditional & Cyber-Bullying………………………………………...14
Cyber-Bullying Characteristics Anonymity…………………………………….……15
Moral Disengagement………………………………………………………………..15
Empathy & Bullying Behaviour………………………………………………...…...17
Self-Compassion……………………………………………………………………..18
Acceptance of Others………………………………………………………..……….20
The Purpose of this Study………………………………………………….…………….…..22
Main Hypothesis……………………………………………………………………………..22
Methods………………………………………………………………………………………24
Participants…………………………………………………………………………...24
Type of Design……………………………………………………………...………..24
Variables……………………………………………………………………………..24
Materials………………………………………………………………………………….…..25
Traditional Vs Cyber-Bullying 3
Smith’s Traditional & Cyber-Bullying Survey………………………………………25
Interpersonal Reactivity Index Survey………………………………………………26
Moral Disengagement Survey………………………………………………………..26
Acceptance of Others………………………………………………………………...27
Self-Compassion Scale-Short Form………………………………………………….27
Setting & Procedure………………………………………………………………………….27
Results……………………………………………………………………………………….30
Overlap of Bully Groups with Cyber-Bully Groups…………………………………30
Relationship between Gender and Cyber-Bullying Groups………………………….31
Relationship between Moral Disengagement & Interpersonal Variables…………...33
Relationship between different Bullying Groups & Interpersonal Variables………..33
Relationship between Gender & interpersonal Variables…………………………....34
Relationship between Gender & Moral Disengagement…………………………......35
Relationship between Nationality & Moral Disengagement…………………………35
Relationship between Nationality & Interpersonal Variables……………………..…35
Frequencies and experiences of Bullying, Cyber-Bullying, Victimization & Online
Activities…………………………………………………………………………………..…36
Gender & Nationality Frequency with Activities Online………………………….…36
Gender & Nationality Frequency differences and the Places they Use the Internet…36
Frequency of knowing someone who was Bullied & Cyber-Bullied………………...36
Frequency differences of Gender, Nationality & Victimization……………………..37
Overlapping Frequency of Bullying, Cyber-Bullying & Victimization……………...37
Frequency of how long ago were you a Traditional & Cyber-Victim…………….…37
Did you Tell anyone you were Bulling or Cyber-Bullied…………………………....37
Frequency and experiences of a Bully-Victim & Cyber-Bully Others……………....38
Frequency of how long ago did you Bully & Cyber-Bully Others…………………..38
Frequency Experiences of Bully & Cyber-Bullying Others…………………………38
Traditional Vs Cyber-Bullying 4
Frequency of Gender, Nationality & the opinions on; What do you think are the best
ways to stop traditional Bullying & Cyber-Bullying………………………………………...39
Frequency of the Opinions of Participants whether Cyber-Bullying is more harmful
than Bullying………………………………………………………………………………....39
Realationship between Bullying and Cyber-bullying groups and time spent online..39
Relationship between Nationality and Bullying & Cyber-Bullying Groups………..40
Relationship between Cyber-Bullying and the time spent online……………………40
Relationship between Cyber-Victims and the time spent online…………………….41
Discussion………………………………………………………………………………........43
References……………………………………………………………………………………53
Appendix……………………………………………………………………………………..60
List of Figures
Figure 1: Results of a Two-Way Contingency Analysis Examining Bully/Non Bully & CyberBully/Non-Cyberbully………………………………………………………………………31
Figure 2A: Results of a Two-Way Contingency Analysis Examining Gender &Bullying….31
Figure 2B: Results of a Two-Way Contingency Analysis Examining Gender & CyberBullying………………………………………………………………………………………32
Figure 3: Results of a Two-Way Contingency Analysis Examining Cyber-Bullying, NonnCyberbullying & time spent online……………………………………………………….41
Figure 4: Results of a Two-Way Contingency Analysis Examining Cyber-Victims & time
Spent online………………………………………………………………………………….42
List of tables (Appendix)
Table 1A: Participants Characteristics by Nationality & Gender…………………………….60
Table 1B: Participants Characteristics by Nationality & Current Status…………………….60
Table 1C: Participants Characteristics by Nationality and Time Spent Online Per Week….60
Table 2A: Gender & Activities Online…………………………………………………………...…61
Traditional Vs Cyber-Bullying 5
Table 2B: Nationality & Online Activities………………………………………………………....62
Table3A: Gender & Place you like to use the Internet………………………………………...…63
Table3B: Nationality & Place you like to use the internet…………………………….............64
Table 4A: Total Responses to knowing someone who was Cyber-Bullied…………………..…65
Table 4B: Total Responses to know someone who was Cyber-bullied…………………………65
Table 5A: Gender & Traditional Bully-Victims………………………………………………..…66
Table 5B: Gender & Cyber-Victims………………………………………………………………..67
Table 5C: Nationality & Traditional Bully-Vitcims…………………………………………..….68
Table 5D: Nationality & Cyber-Victims…………………………………………………….……..69
Table 6A: Overlapping of Bullying-Victim & Bully, Non-Bully………………………….…….70
Table 6B: Overlapping of Bully-Victim & Cyber-Bully, Non-Cyberbully……………………..71
Table 6C: Overlapping of Cyber-Victim & Bully, Non-Cyberbully…………………..………..72
Table 6D: Overlapping of Cyber-Victim & Cyber-Bully, Non Cyber-bully……………………73
Table 7A: How long ago were you Bullied...........................................................................74
Table 7B: How long ago were you Cyberbullied………………………………………….……..74
Table 8A: As a Bully-Victim, what experiences did you have………………………………….75
Table 8B: As a Cyber-Victim, what experiences did you have………………………………….76
Table 9A: How long ago did you Bully someone…………………………………………………77
Table 9B: How long ago did you Cyber-Bully someone………………………………………….77
Table 10A: What Behaviour did you engage in as a Bully…………………………….……....78
Table 10B: What Behaviour did you engage in as a Cyber-Bully…………………………….79
Table 11A: Opinions on how a Victim of Bullying might feel…………………………………..80
Table 11B: Opinions on how a Victim of Cyber-Bullying might feel…………………………..81
Table 12A: Opinions on how best to stop Bullying (Gender)……………………………………82
Table 12B: Opinions on how best to stop Cyber-Bullying (Gender)……………………………83
Table 12C: Opinions on how best to stop Bullying (Nationality)……………………………….84
Traditional Vs Cyber-Bullying 6
Table 12D: Opinions on how best to stop Cyber-Bullying (Nationality)………………………85
Table 13A: Males Opinions on whether Cyber-Bullying is more harmful than Bullying……86
Table 13B: Female Opinions on whether Cyber-Bulling is more harmful than Bullying……87
Table 13C: European Opinions on whether Cyber-Bullying is more harmful than
Bullying………………………………………………………………………………………...88
Table 13D: Asian Opinions on whether Cyber-Bullying is more harmful than Bullying…….89
Traditional Vs Cyber-Bullying 7
ACKNOWLEDGEMENTS
The completion of this dissertation was made possible thanks to many individuals. I would
like to extend my thanks first off to my supervisor Margaret Walsh for her unrelenting
assistance and confidence in my abilities with my dissertation. She has played an
instrumental role of motivation for me in the field of psychology and I appreciate her
contributions to my psychological studies over the years. Finally, I would like to thank my
family and friends for their unyielding support, friendship, encouragement and there eternal
emotional support throughout my education.
Thanks Guys
Traditional Vs Cyber-Bullying 8
Abstract
Though an extensive amount of research has been conducted on traditional
playground bullying (Olweus, 1993), very little academic research has been conducted on
cyber-bullying. This study aims to investigate the associations among traditional and cyberbullying groups, cognitive mechanism, specifically moral disengagement and interpersonal
variables. Using quantitative comparative correlation non-experimental design a sample of
107 participants (European and Asian nationalities) completed the Smith’s Traditional and
Cyber-bullying Scale (Smith et al, 2008), Interpersonal Reactivity Index (Davis, 1980), the
Moral Disengagement Scale (Bandura, et al. 1996), Acceptance of Others Scale (Fey, 1955),
and Self-Compassion Scale-Short Form (Raes, et al. 2011). Result found an overlapping of
bullying/victim gorups, male and females were involved in bullying and victim groups. More
males reported to be bully and cyber-bullies. There was no significant difference between
bullying groups and MD and MD and interpersonal Variables. Cyber-Bully Groups scored
higher on MD mean scores, female’s score higher on interpersonal variables than
males.Implications of findings was discussed and directions for future research are suggested.
Traditional Vs Cyber-Bullying 9
Introduction
Traditional bullying in schools is not a new phenomenon and has been well
established as a common and serious problem in society. Traditional bullying, repeated act or
behaviour of intentional aggression that is carried out by a group or an individual and over
time against a victim who cannot easily defend him or herself (Olweus, 1993).
History of Bullying:
This is a topic which has gained much interest in Europe since the 1970’s when the
works of Scandinavian psychologist Olweus was published in 1978, titled “Aggression in the
schools: bullies and the whipping boys”. Work continued in the Scandinavian regions, and
1980’s research lead to the first example of a national intervention campaign against bullying.
This and related works are described by Olweus in his book “Bullying at School: What we
know and what we can do” (Olweus, 1993). The success of this Norwegian work
undoubtedly influenced and inspired the subsequent research intervention activities in other
European countries. Which resulted in an increased awareness of bullying and victimization
as a wide spread social and national problem for countries researching the topic. For a
number of years, research on bullying was conducted primarily in Sweden and other
Scandinavian countries (Olweus, 1993). In Europe, a meeting hosted in Stavanger, Norway,
in 1987 acted as encouragement to other researchers and practitioners. Finland, the United
Kingdom, and Ireland, in particular, started developing programs of work (Smith et al.,
2000). Beginning in the early 1990’s other countries including Japan, Australia, Canada and
the U.S., began the studying of bullying behaviours also (Olweus, 1993).
In Ireland, there were a number of surveys and research carried out on bullying over
the last two decades, results showed from O’ Moore study at the Anti-Bullying Centre in
Trinity College Dublin found that some 31% of primary students and 16% of secondary
students have been bullied at some time. From 870,000 Irish students, approximately 23% or
Traditional Vs Cyber-Bullying 10
200,000 children are at the risk of suffering from bullying behaviour. Bullying can occur
almost anywhere, but where there is no adult supervision it thrives. Traditional bullying is
considered to be a playground activity. Results from the nationwide study of bullying
behaviour in Irish schools showed this shift from inside to outside bullying, O’ Moore found
that in Primary schools in 74% of students who said they were bullied, reported that the
bullying occurred in the playgrounds, 19% said they were bullied going to and from school,
while 8.8% of post-primary said the same. At secondary level 47% of students report that
bullying behaviour was most common in the class room, 37% was in the corridors and 27%
reported in the playground (Anti-Bullying Centre: Trinity Collage Dublin, n.d).
A rather separate tradition of research was developed in Japan. A Japanese word,
“ijime”, corresponds closely to the English word bullying. During the 1980’s in Japan, there
were surveys on the nature and frequency of ijime, which was believed to be a specifically
Japanese problem only. Some findings based on teacher reports suggested a decrease in the
problem, and research activity and public concern declined. However, a succession of
suicides caused by bullying from 1993 to 1995 led to a second phase of activity that currently
continues (Smith et al, 2000).This time around, there has been much interchange between
Japanese and western researchers as it was better understood to be a international growing
concern, with joint research activities and publication in 1999 entailed “The nature of school
bullying: a cross-national perspective” (Smith et al, 2000). These statistics confirm that
school bullying and violence is not just a national problem but it is a universally recognized
international problem.
Limber et al, (2003) found historically bullying in schools was considered to be a
problem that needed attention yet it was also considered a part of normal childhood in the
eyes of adults, referring to it as “rite of passage” for children (p. 445). As a result of this
Traditional Vs Cyber-Bullying 11
increased research along with an increase in attention by the mass media, society began to
recognize and then to attend to the harmful effects of bullying and victimization on youth. A
view on bullying “rite of passage” belief changed in western societies when in America after
a decade of school shooting related violence during the 1990’s, of which Columbine High
School massacre on April 20th 1999 being the most violent (BBC News, 2007). Following
these events, anti-bullying programs and zero-tolerance policies were introduced into schools
to help combat and eliminate bullying in America. Hanweld (2009) argues that when a total
ban of bullying in schools was taken place throughout the globe and the parallel fast growing
advancement in networked communication technologies had arrived in the 1990’s. With easy
and fast accessibility and cheap costs this may have been a contributing factor to a shift from
these traditional bullying behaviours into a new online cyber playground.
Cyber-bullying:
Cyber-bullying has been defined as “an aggressive, intentional act carried out by a
group or individual, using electronic forms of contact, repeatedly and over time against a
victim who cannot easily defend him or herself” (Smith, et al., 2008, p.376). Smith et al.
(2008) looked at seven electronic media’s of cyber-bullying as a tool for carrying out
bullying behaviours online, including phone calls, text messages, picture/video clips, emails,
instant messaging, website and chat rooms. Phone call and text message bullying were most
frequent, with instant messaging bullying and their impact being perceived as comparable to
traditional bullying. It was found that mobile phone/video clip bullying though rarer was
perceived to have a more negative impact than other media communication bullying.
According to Smith et al, (2008) study found with British students reported that incidence of
being cyber-bullying was less frequent than traditional bullying, but that bullying behaviour
however happened more frequently outside of school than inside. Also being a cyber-victim,
Traditional Vs Cyber-Bullying 12
but not a cyber-bully, correlated with internet use and many cyber-victims were traditional
‘bully-victims’.
Types of Bullying:
Research on face-to-face bullying, also known as traditional or conventional bullying,
has covered several types and forms of the behaviour, including direct physical aggression,
verbal and relational bullying. Past research focused on two forms of bullying: verbal and
physical. Verbal bullying encompasses behaviours such as name calling, taunting, threatening
or degrading while physical bullying comprises actions such as punching, kicking,
vandalizing, performing rude gestures, and making faces (Olweus, 1991). Relational
bullying focuses on gossiping, spreading rumours, social exclusion, and other acts intent on
damaging relationships (Scheithauer et al, 2006, Bjorkqvist et al, 1992). A quite new
phenomenon and form of bullying, cyber-bullying is known to be a “the by-product of the
union of adolescent aggression and electronic communication” (Hinduja et al, 2008, p. 131).
Incidence Rates of a Universal problem Bullying & Cyber-bullying
According to Ybarra et al. (2004) surveyed internet use in 1,501 youths aged 10–17
years and found over the last year, 12% reported being aggressive to someone online, 4%
were targets of aggression, and 3% were both aggressors and targets. In Northern Ireland the
present data clearly indicates that the incidence of victimization (76.8%, n = 305) within a
peer group was clearly high compared to previous research carried out in Northern Ireland
(Mc Gukin et al., 2006). In Germany, of 2086 fifth to tenth graders, 12.1% of the students
reported bullying others and 11.1% reported being bullied (victimisation) (Scheithauer et al.,
2006). The prevalence of frequent involvement in bulling appears to increase in late
elementary schools, peak during middle school, and decline in high schools (Olweus, 1993).
Traditional Vs Cyber-Bullying 13
Therefore with the increase with age in traditional bullying there seems to be a decrease in
bullying behaviours. In Japan a total of 23,258 incidents of bullying were reported in the
1992-93 academic year in Japan. Kumagai points out that the reported bullying incidents
occurred most frequently at junior high schools (13,632 cases, 32.5% of the total cases in
public schools), followed by senior high schools (2,326 cases, 23.6%) and elementary schools
(7,300 cases, 11.8%) (Kumagai et al, 1996). “Research findings have emerged mostly from
English-speaking countries while other parts of the world (i.e. Asia) are just beginning to get
underway” (Hanweld, 2009, p.12). Thus the need for more research on the sparse area of
cyber-bullying is essential for our better understanding of this new form of bullying as well
as the particular need for research with Asian samples (Ang et al., 2010). However media has
highlighted the problem of bullying behaviour worldwide and that bullying is in fact a
universal global problem.
Gender differences in traditional and cyber-bullying:
Tradition:
One interesting aspects of bullying and cyber-bullying debate is gender differences.
As previously noted, research on bullying by girls was initially lacking due to the belief that
girls did not partake in bullying behaviour. This was mainly due to the exclusive focus on
bullying as an overt (physical or verbal) act. However, when physical, verbal, and relational
bullying was considered, the research illustrates that girls can bully just as much as boys (Rys
et al., 1997). In fact, many studies have found that while boys are more likely to use direct
physical and verbal aggression, girls are more likely to use relational aggression in their
bullying (Bjorkqvist 1992, Rys et al., 1997). Pornari et al, (2010) results found that boys (M=
511.32) reported more direct aggression than girls (M= 510.09) and girls (M= 57.24) slightly
more indirect aggression than boys (M= 56.70).
Traditional Vs Cyber-Bullying 14
Cyber-bullying
Girls were more likely to be victims in cyber-bullying compared to boys (Smith, et al.
2008; Li, 2005). According to Ybarra et al. (2004) found in a United States sample, males
and females were equally likely to report harassing another person online in the past year. In
contrast, Li (2006) research found Canadian sample of 264 students, 22% of males reported
more cyber-bullying behaviour than females 12%, however 25 % of males and 25.6% of
females reported being cyber-victims. Although there was no gender difference for
victimisation at all, boys reported significantly more often than girls being bullied physically
(Scheithauer et al., 2006). Bjorkqvist (1992) findings was that girls of the two older cohorts
overall make greater use of indirect means of aggression, whereas boys tend to employ direct
means. The percentages of 396 Singapore adolescent boys and girls involved in cyberbullying were 23.6 and 15.1%, respectively.
Overlap of Traditional and Cyber-bullying:
Recent studies have also demonstrated that there is an overlap between types of
bullying such as that cyber-victims were traditional victims, many cyber-bullies were
traditional bullies (Smith et al., 2008; Ybarra et al., 2004). Both traditional bullying and
cyber-bullying have the same basic motivation: retaliation (revenge for having been violated)
and the perpetrator's desire for power and control (Shariff, 2008). Konig et al. (2010)
confirmed this when looking at the overlaps between victims of traditional forms of bullying
and cyber-bullying with German students, data from 473 students was collected. Of these,
149 were identified as traditional victims/cyber-bullies. Results show that traditionally
bullied students indeed tend to choose their former perpetrators as cyber-victims, and that
individual differences play a role in the choice of their victims.
Traditional Vs Cyber-Bullying 15
Cyber-bullying characteristic Anonymity:
A unique characteristic of cyberbullying is anonymity or the ability to hide ones
identity online behind technologies, putting a distance between the perpetrator and the victim.
Li (2005) found that out of 177 Canadian students over 40% did not know the identity of
their online perpetrator and the perpetrator will not be able to observe the victims reactions or
the consequences of their actions. According to Ybarra et al, (2004) reported from their
findings that the victims of real-life bullying turned into online perpetrators. This rolereversal seems intuitively right as it allows face-to-face bullied victims revenge through
cyber-bullying while hiding behind their computer screens. Protected by anonymity, the
previously powerless victim in traditional bullying situations turns to a powerful perpetrator
online, thus spreading the cruelty to many online. However, it was suggested by Ybarra that
the correlation between face-to-face bulling and cyber-bullying with subsequent perpetuation
of viciousness needs further investigation.
Moral Disengagement:
Bandura (1999) describes moral disengagement in the perpetrations of inhumanities.
Bandura suggest that moral agency is manifested in both the power to refrain from behaving
inhumanely and the proactive power to behave humanely. “Moral agency is embedded in a
broader socio-cognitive self theory encompassing self-organizing, proactive, self-reflective,
and self-regulatory mechanisms rooted in personal standards linked to self sanctions. The
self-regulatory mechanisms governing moral conduct do not come into play unless they are
activated, and there are many psychosocial maneuvers by which moral self-sanctions are
selectively disengaged from inhumane conduct. The moral disengagement may centre on the
cognitive restructuring of inhumane conduct into a benign or worthy one by moral
justification, sanitizing language, and advantageous comparison; disavowal of a sense of
Traditional Vs Cyber-Bullying 16
personal agency by diffusion or displacement of responsibility; disregarding or minimizing
the injurious effects of one's actions; and attribution of blame to, and dehumanization of
those who are victimized” (Bandura, 1999, p. 193).
Figure1. Mechanism through which moral self-sanctions are selectively activated and disengaged from
detrimental behaviour at different points in the self-regulatory process (Bandura, 1999, p. 194).
Gini (2006) found that moral disengagement showed higher levels of the tendency to
disengage self-sanctions and justify the use of aggressive behaviours in all the aggressive
roles and, in particular, in bullies.
According Menesini (2003) research findings confirmed previous findings in this area
that bullies show higher levels of moral disengagement as compared to victims. It was found
an absence of moral emotions and sense of responsibility in bullies as compared to other
children. Menesini analyses found that justifications revealed that bullies have a profile of
egocentric reasoning that is particularly evident when they justify attribution of
disengagement to self in the role of the bully. It seems that when they think about themselves
in this role, personal motives and the advantages of bullying behaviour are sufficient to
justify negative and detrimental behaviour. “Children and in particular bullies reported that
Traditional Vs Cyber-Bullying 17
they would feel proud or indifferent simply because they reason in an egocentric and selfish
way and value the personal benefits of these actions” (Menesini, 2003, p. 524).
Turner et al. (2007) results showed looking at the correlations among moral
disengagement, aggression and pro-ocial behaviour. That bullies (M= 75.81) scored highest
on the mean moral disengagement scores across the bully/victim continuum. Bully/victims
came scored highest with a score of (M= 67.41) and Victims scored (M= 61.97). Moral
disengagement was associated with bullying and aggression and was negatively correlated
with prosocial behaviour.
Perren, et al. (2011) summarized that cyber-bullies have even stronger deficits in their
morality than bullies who only use traditional means. Traditional peer aggression positively
related to children’s moral justification, euphemistic language, displacement of responsibility
and outcome expectancies, and negatively associated with hostile attribution bias. Moral
justification also related positively to cyber aggression and cyber-aggression and cybervictimization were associated with high levels of traditional peer aggression and
victimization, respectively (Pornari et al, 2010).
Empathy and Bullying Behaviours:
“Empathy has been defined as sharing of another person’s emotional state, that
empathy as a multidimensional construct with cognitive and emotional components that have
to be taken into account to understand emotional responsiveness” (Steffgen et al, 2009, p. 2).
Steffgen (2009) results from a sample of 2070 Luxembourg students found that cyberbullies
show a greater lack of empathy for others being victimized than do non-cyberbullies, (F (1,
2,065) =31.97; p < .001), no gender difference was found. Also traditional bullies show a
greater lack of empathy than non-bullies (F (1, 2,063) = 20.17; p < .001) boys and girl bullies
was significantly associated with low levels of empathtic responsiveness, but traditional
Traditional Vs Cyber-Bullying 18
bullies had more empathy than cyber-bullies. This study shows a negative relationship
between empathy and aggression.
Both victims and bullies of a 71 German student sample showed significantly less
empathy than students not involved in cyber-bullying. Mean values of empathy (N=61)
reported was cyber-bullies (M= -1.91) versus non-bullies (M=0.20) and cyber-victims (M= 3.51) versus non-victims (M= 0.39) (Schultze-Krumbholz, 2009).
Caravita et al, (2009) results from a sample of 461 students in Italy showed that
traditional Bullying was negatively linked to affective empathy (b = -.19, p < .05) and social
preference (b = -.44, p < .01), and positively to perceived popularity (b = .50, p < .001),
whereas defending was positively associated with both affective empathy (b = .28, p < .01)
and social preference (b = .47, p < .001). The results confirmed that individual (empathy) and
interpersonal (social status) variables were found to interact in predicting bullying and
defending.
Research found that males who were non-bullies in face-to-face bullying had mean
score on the cognitive empathy scale of 32.4 compared to 31.5 for those who reported
bullying. No significance was found. It can be seen that males who where non-bullies scored
higher than those who are bullies on cognitive, affective and total empathy, but none of these
differences were significant (Jollife, 2006).
Self-Compassion:
Neff defines self-compassion as an “emotionally positive self attitude that should
protect against the negative consequences of self-judgment, isolation, and rumination (such
as depression)” (Neff, 2003, p. 85) Compassion can be extended towards the self when
suffering occurs through no fault of one’s own – when the external circumstances of life are
simply hard to bear. Self-compassion is equally relevant, however, when suffering stems
from our own mistakes, failures or personal inadequacies. Self-compassion entails three main
Traditional Vs Cyber-Bullying 19
components: self-kindness—being kind and understanding toward oneself in instances of
pain or failure rather than being harshly self-critical, common humanity—perceiving one’s
experiences as part of the larger human experience rather than seeing them as separating and
isolating, and mindfulness—holding painful thoughts and feelings in balanced awareness
rather than over-identifying with them. Self compassion also should counter the tendencies
towards narcissism, self-centeredness, and downward social comparison that have been
associated with attempts to maintain self-esteem (Neff, 2003, 2011).
Neff (2003, 2011) found that people’s desire to feel special, better than average, as
high self-esteem would demand for us to feel good, this can be problematic because it is
impossible for everyone to be above average at the same time there will always be someone
who excels more than others. This can cause and inflate on our egos and we tend to put others
down so that we can feel good in comparison. High self-compassion maybe connected to
none bullying behaviour as they would view others with a common humanity and have
mindfulness to have painful thoughts and feelings in a balance awareness and not to over
identify with them, and to have self-kindness and to not blame others for our own failures in
life but be kind and understanding to one self rather than self critical.
Neff (2003) Research indicates that self-compassion tends to be slightly (but
significantly) lower among women than men, examining levels of self-compassion and its
association with psychological well-being in Thailand, Taiwan, and the United States. Mean
self-compassion levels were highest in Thailand and lowest in Taiwan, with the United States
falling in between. In all three cultures, however, greater self-compassion predicted
significantly less depression and greater life satisfaction. These findings imply that there may
be universal benefits to self-compassion despite cultural differences in its prevalence,
although research is needed into this question.
Traditional Vs Cyber-Bullying 20
Pauley, (2010) found there participants reported that they thought having compassion
for themselves felt meaningful in relation to their experiences and useful in helping with their
depression or anxiety.
Neff, (2003) highlighted previous research in individual and group differences of selfcompassion that the ability to recognize and attend to internal feeling states is linked to the
empathy that children receive from their caregivers early on. This suggests that individuals
who experienced warm, supportive relationships with their parents as children, and who
perceived their parents as understanding and compassionate, should tend to have more selfcompassion as adults.
Acceptance of Others:
Attitudes of acceptance of others, “communicating acceptance between people creates
feelings of emotional safety. In such an atmosphere one can relax and discuss herself without
fear of evaluation” (Matthews, 1993, P. 2).
Results found that participants who have a high score on acceptance of self tend’s also
to accept others. Individuals with high acceptance-of-others scores tend to feel accepted by
others and tend toward being accepted by them (Fey, 1955). Veenstra, (2010) found that
acceptance and rejection are not tied to the same process. Male bullying was positively
related to peer rejection by the gender to which the victim belonged, but not negatively to
peer acceptance. Bullies are not rejected in general, but only by those for whom they are a
potential threat. Bullies seem to choose their victims so as to minimize loss of affection.
Bullies will pick victims that are rejected by their same-gender classmates. Findings indicated
that after controlling for the overlap between peer social standing and self-perceived social
acceptance, children who reported higher levels of self-perceived social acceptance exhibited
higher levels of peer-rated fighting (Pardini, 2006). Olthof, (2008) result found that children’s
bullying behaviour would be positively related to their desire to be accepted by other bullying
Traditional Vs Cyber-Bullying 21
children, while being unrelated or negatively related to their desire to be accepted by nonbullying children. These findings raise the issue of why boys’, but not girls’,
bullying/following behaviour was related to their desire to be accepted by same-sex children
with the same behavioural style. In contrast it was found that girls bullying/following
behaviour was related to their desire to be accepted by bullying/following boys, and also their
desire to be accepted by other boys. This interpretation suggests that girls’ bullying/following
behaviour might be related more strongly to their desire to be accepted by boys showing
bullying/following behaviour than to their desire to be accepted by non bullying boys.
Traditional Vs Cyber-Bullying 22
The purpose of this present study
Research on cyber-bullying is still in infancy, specifically exploring the relationships
of cyberbullying and traditional forms of bullying and there associations to cognitive
mechanism and interpersonal variables specially using an Asian sample. Therefore the
purpose of this study is to examine; 1) exploration of the incidence rates of cyber bullying
and victimization. 2) Further examination of the likelihood that adults who are engaging in
face-to-face bullying are also engaging in online bullying and those who are victims also
might be involved in bullying behaviours. 3) Examining gender and nationality differences in
cyber and traditional forms of bullying/victimization groups. 4) The overlap between face-toface and cyber forms of bullying. 5) Examining moral disengagement among bullies, nonbully, cyber bully and non-cyberbullies. 6) Examining also if there is a relationship between
high moral disengagement scores, this maybe a contributing factor for low empathy scores,
low self-compassion and ones attitude towards acceptance of others. 7) Examining if low
empathy, self compassion and acceptances of others score maybe a contributing factor of
bullying behaviour. The opposite of these interpersonal scores should determine none
involvement in aggressive bullying behaviour. Cyber-bullying unique characteristic should
contribute to higher scores in moral disengagement and lower scores in interpersonal
variables compared to traditional bullying. Gender differences will be observed as passed
research shows inconsistency.
The aim of this study is to investigate the associations between both types of bullying,
cognitive mechanism and specifically moral disengagement and interpersonal variables
specifically, empathy, self-compassion and acceptance of others from two different
nationalities groups and gender and to examine the relationship between moral
disengagement and interpersonal variables. Gender and nationality differences will be
observed.This investigation is conducted with the hope that this study will enhance the
Traditional Vs Cyber-Bullying 23
knowledge of this new cyber-bullying field and of the fields of moral disengagement and
interpersonal psychology, and yield useful information with implications for improvement
and development of cyber-bullying prevention methods.
Main Hypothesis
1. It is hypothesized that there is an overlap between traditional bullies/non-bullying and cyberbullies/non cyber-bullying.
2. It is hypothesized that both males and females will be involved in traditional and cyberbullying groups, but it is predicted that females will be more likely than males to be involved
as cyber-bullies, because it is a more indirect form of peer aggression.
3. It is hypothesized that cyber-bullying types will score higher on the moral disengagement
scale than traditional bullying types or those not involved in either type of bullying.
4. It is hypothesized that high scores on moral disengagement will result in low scores in the
interpersonal variables (empathy, self compassion, acceptance of others). And we predict
moral disengagement to be negatively correlated to these interpersonal variables in both types
of bullying.
5. It is hypothesized that high scores in the interpersonal variables (empathy, self compassion,
acceptance of others) will be negatively correlated to bullying types of behaviour.
6. It is hypothesized that those who report traditionally bullying others will have higher scores
on the interpersonal variables than those who reported cyber-bullying others.
7. It is hypothesized that females will have higher scores on the interpersonal variables than
males.
Traditional Vs Cyber-Bullying 24
Method
Participants
The sample consisted of a snowball and convince random sample of 107 people (ages
18 to 64) from three current statuses, working (N=40, 37.4%), attending college/university
(N=45), and both (N=20), two participants (1.9%) failed to identify their current status. Of
this sample, 45 (42.1%) were male and 62 (57.9%) were female. Participants were of Asian
(N= 37, 34.6%) nationality and European (N= 70, 65.4%) nationality. The majority of
European nationals sample consisted predominantly of Irish (N= 55, 51.4%), with small
representation of British (N=1, 0.9 %), French (N=1, 0.9%), Scottish (N=3, 2.8%). Spanish
(N=2, 1.9%), Italian (N=2, 1.9%), Polish (N=2, 1.9%), German (N=1, 0.9%) and Slovakian
(N=2, 1.9%). The majority of Asian nationals consisted predominantly of Chinese (N=19,
17.8%), with small representation of Filipino (N=4, 3.7%), Japanese (N=8, 7.5%), Taiwanese
(N=1, 0.9%), Mongolian (N=2, 1.9%), Malaysian (N=3, 2.8%).
Type of design
A quantitative comparative correlation non-experimental design (surveys).
Correlated variables:
Dependant Variables: moral disengagement (1: strongly agree,2: agree, 3:disagree,4: strongly
disagree), empathy (A: does not describe me well, B: describes me a little, C: describes me,
D: describes me well, E: describes me very well), self compassion (1: almost never, 2:
sometimes, 3: often, 4: very often, 5: almost always), acceptance of others (strongly agree,
agree, disagree, strongly disagree), Independent variables; include cyber-bullying (cyberbully, cyber-victim, cyber-bully/victim and non cyber-bully, non cyber-victim), traditional
bullying (bully, victim, bully/victim, non bully, non victim), demographic variables; ( age,
Traditional Vs Cyber-Bullying 25
gender, nationality, general information about internet use (hours per week, place internet
access, activities online, ability of computer use).
Measures & Questionnaires
Analyses were conducted using SPSS for Windows, version 18.
Demographic Information: Participants were asked to fill out a demographic
questionnaire that was developed by the primary investigator specifically for this study
regarding their gender, age, nationality (Asian, European), nationality specifically (e.g., Irish,
Chinese), and current status (working, attending college/universities, both).
Smith’s Traditional and Cyber-bullying Scale
Smith’s bullying/cyber-bullying scale is a 19 item questionnaire. “After initial online
demographic questions, there was a definition of bullying, followed by a statement about
cyber-bullying as including the seven media: through text messaging; pictures/photos or
video clips; phone calls; email; chat rooms; instant messaging; and websites. Two general
questions asked whether the pupil had experienced bullying of any kind, and then specifically
cyber-bullying, in the past couple of months (5 point scales: never; only once or twice; 2 or 3
times a month; about once a week and several times a week). Multiple-choice questions
asked, for each of the seven media, how often pupils had experienced being cyber-bullied or
had cyber-bullied others (same 5-point scales), separately for inside and outside school;
whether they had heard of that type of cyber-bullying taking place in their school or circle of
friends in the past couple of months (yes/no); the perceived impact compared to traditional
bullying (less, the same, more); the number, gender and class of those who had cyber-bullied
them; how long it had lasted; whether and whom they had told; and whether they felt that
banning mobile phones or internet in school would help to avoid that type of cyber-bullying.
Traditional Vs Cyber-Bullying 26
Open-ended questions allowed pupils to give more detailed answers on examples of cyberbullying, reasons for perceived impact, and suggestions for stopping it. The time-frame was
the ‘past couple of months’” (Smith, et al. 2008, p.377-378).
Interpersonal Reactivity Index (IRI) Scale
All subjects in this study completed the IRI, a 28-item self-report questionnaire
consisting of four 7-item subscales, each of which assesses a specific aspect of empathy. The
Perspective-Taking (PT) scale measures the tendency to adopt the point of view of other
people in everyday life. The Fantasy (FS) scale measures the tendency to transpose oneself
into the feelings and actions of fictitious characters in books, movies, and plays. The
Empathic Concern (EC) scale measures the tendency to experience feelings of warmth,
compassion, and concern for other people. The Personal Distress (PD) scale also assesses
typical emotional reactions, but rather than other-oriented feelings of concern, it taps one's
own feelings of personal unease and discomfort in reaction to the emotions of others. A five
point likert scale, (does not describe me very well, describes me a little, describes me,
describe me well, describes me very well) was used to rate each person response to the
statements (Davis, 1983).
The Moral Disengagement Scale
All subjects in this study completed the Moral Disengagement scale, a 32-item selfreport questionnaire consisting of eight 4-item subscales, each of which assesses a specific
aspect or mechanisms of moral disengagement. These mechanisms include moral
justification, euphemistic language, advantageous comparison, displacement of
responsibility, diffusion of responsibility, distorting consequences, attribution of blame and
dehumanization. A four point likert scale, (strongly agree, agree, disagree, strongly disagree)
was used to rate each person response to the statements (Bandura et al, 1996). *Note*: Moral
Traditional Vs Cyber-Bullying 27
Disengagement Scale was coded in spss as (strongly agree = 1, agree = 2, disagree = 3,
strongly disagree = 4). Thus those scoring high on the moral disengagement would strongly
disagree with moral disengagement scale. Internal consistency reliability for the Moral
Disengagement Scale has been reported as .82 (Bandura et al, 1996).
Acceptance of Others Scale
All subjects in this study completed the Acceptance of Others scale, a 20-item selfreport questionnaire consisting of 20 statements that measure some of your feelings and
attitudes about other people. A four point likert scale, (strongly agree, agree, disagree,
strongly disagree) was used to rate each person response to the statements (Fey, 1955).
Self-Compassion Scale-Short Form (SCS-SF)
All subjects in this study completed the Self-Compassion Scale-Short Form, a 12-item
self-report questionnaire consisting of six 2-item subscales, Self-Compassion Scale – Short
(12 items instead of 26 items) the short scale has a near perfect correlation with the long scale
when examining total scores. The subscales of (SCS-SF) includes, self-kindness items, selfjudgment items, common humanity items, isolation items, mindfulness items and overidentified Items. A five point likert scale, (almost never, sometimes, often, very often, almost
always) was used to rate each person response to the statements (Raes, et al. 2011).
Setting & Procedure
The study was approved by The Dublin Business School Review/Ethics Board and
data collection occurred in February/March, 2012. This research was conducted via
questionnaire online hosted by “kwiksurveys” and through paper questionnaires distribution.
Participants where sought from Dublin Business School, Dublin Trinity Japanese Society and
Dublin Trinity Chinese Society. An email was sent to each of the college administrations to
ask permission for access to the students. The presidents of both Trinity Societies and Dublin
Traditional Vs Cyber-Bullying 28
Business School administration gave permission to access of international students. A day
and time was arranged through the administration and international office in Dublin Business
School to hand out paper questionnaire to a class of mix European and Asian nationals giving
a random convince sample. Questionnaire packages were administered by the principal
researchers in February 2012. Each student completed the entire battery of questionnaires in
20-25 minute sessions. The principal researchers administered the questionnaires to each
participant in the mixed nationality class, quickly reviewing the aims of the study. Aside from
one group which completed their questionnaires in a classroom, the remainder of the
participants of paper surveys completed questionnaires in the library. To ensure that surveys
were completely anonymous and confidential, participants did not submitted identifying
information, also participants could read about this on the front page of the questionnaire
booklet. Participants were informed that the surveys would be used to learn about traditional
versus cyber-bullying: associations with cognitive mechanisms and interpersonal variables in
European and Asian Sample and help contribute to the knowledge in these areas of
psychology. Trinity Asian Societies agreed to email there members the link to the online
questionnaire giving a snowball sample and they were asked to pass it on to friends who may
be willing to take part. The online questionnaire was open to the public of persons over the
ages of eighteen which was indicated on the cover page of the booklet. Participants were
informed on the questionnaire instructions online and on paper that participation was
voluntary and informed that they could withdraw participation at any time during the study
without consequence. The online questionnaire instruction on the first page was the same
instructions as paper questionnaires.
Due to a technical error during the survey reproduction process, one item from the
Moral Disengagement Scale (question 32) was not included in the questionnaire package.
Traditional Vs Cyber-Bullying 29
The question was a part of the attribution of blame sub category, (children are not at fault for
misbehaving if their parents force them too much). The omission of this item did not affect
the reliability of the total moral score. A Cronsbach alpha test was carried out to check the
reliability of the scale with only 31 items instead of 32 items. Cronsbach alpha reliability test
reported .898 and cronsbach alpha based on standardize items was .902. Indicating the moral
disengagement scale was still reliable.
Traditional Vs Cyber-Bullying 30
Results
Thus results of frequency in these sections of Smiths cyber-bully/traditional bullying
questionnaire include questions which participants multiple responses where to be put into
multiple response sets. First the hypothesis results will be given, followed by Nationality and
gender differences in moral disengagement. Following this frequency of Traditional and
Cyber-bullying and online activities as well as experiences and opinions on bullying and
victimization.
Overlap of Bully groups with Cyber-bully groups
A two way contingency table analysis was conducted to examine the relationship
between bully/non-bully groups and cyberbully/non-cyberbully groups. Person’s chi-square
was conducted to analysis the relationship between bully/non-bully groups and
cyberbully/non-cyberbully groups overlapping. (Pearson’s chi-square; χ2 (1, N=105) =
19.571, p<.05, V= .432) indicating that bully/non-bully was significantly related to
cyberbully/non-cyberbullying groups. Taking part in bullying (V=.432) has a moderate to
strong effect on taking part in Cyber-bullying. The results found from (N=105) participants
reported the following overlapping of groups, bully/cyberbully (N=9), non-bully/noncyberbully (N=16), non-bully/cyberbully (N=3), non/bully/non-cyberbully (N=77).
Overlapping of bullying groups is reported in detail see (Figure 1).
Traditional Vs Cyber-Bullying 31
Figure 1. 2x2 contingency table analysis
Bully/non Bully & Cyber-bully/Non-Cyberbully
Have you ever taken part in cyberbullying
Have you ever taken part in
bullying
yes
no
Total
yes
9
16
25
no
3
77
80
12
93
105
Total
Note. Missing Value (N=2)
Relationship between Gender and Cyber-bullying groups:
Two-way contingency table analyses examining differences between males and
females and bully/non-bully groups. Chi square test found a significant association between
males and females and bully/non-bully groups (Pearson chi square χ² (1, N = 105) = 9.927, p
<.05, V = .307) indicating that Gender was significantly related to taking part in bullying.
Gender (V=.307) had a moderate effect on taking part in bullying (Bully/Non bully Groups)
Males reported higher frequency of involvement in bullying (N=17) compared to females
(N=8). For detailed analysis of frequency see (Figure 2A).
Figure 2A. 2x2 contingency table analysis
Gender & Bullying
Have you ever taken part in
bullying
yes
male
female
Total
Note. Missing Value (N=2)
no
Total
17
26
43
8
54
62
25
80
105
Traditional Vs Cyber-Bullying 32
Two-way contingency table analyses examining differences between males and
females and cyber-bullying groups. It was found that there is no significant relationship
between gender and cyber-bullying groups (Pearson chi square χ² (1, N = 107) = .844, ns,
Phi = .089). Males (N=7) reported a higher frequency of involvement in cyber-bullying that
females (N=6). For detailed analysis of frequency see (Figure 2A).
Figure 2B. 2x2 contingency table
Gender & Cyber-bullying
Have you ever taken part in cyberbullying
yes
no
Total
male
7
38
45
female
6
56
62
13
94
107
Total
Relationship between different Bullying Groups and Moral Disengagement
**Please note; that scoring a high mean score on moral disengagement means that
you are not as morally disengaged compared to lower mean scores. Thus low mean scores
indicate more moral disengagement occurring. Moral disengagement was coded as (1
strongly agree to 4 strongly disagree) in spss**.
Analysis of data, using an independent samples t-test found no significant difference
between bully and non/bully groups in relation to total moral disengagement (t = -1.320, df =
97, ns, 2-tailed) indicating statistically no bullying group difference in relation to MD. Bully
group (M = 94.13, SD = 12.92, N=23) reported lower mean scores of total moral
disengagement than non-bully group (M = 97.77, SD= 11.19, N=76). T-test also revealed no
significant difference between cyberbully and non/cyberbully group and total moral
disengagement (t = .795, df = 99, ns, 2-tailed) indicating statistically no cyberbullying group
Traditional Vs Cyber-Bullying 33
difference in relation to MD. cyberbully group (M= 95.63, SD=12.4, N=11) reported lower
mean scores of total moral disengagement than non-cyberbully groups (M=97.15, SD=11.56,
N=90).
Relationship between Moral Disengagement and Interpersonal Variables
Analysis of data, a Pearson’s r test was used to test the strength of the relationship
between total moral disengagement and the total interpersonal variables (empathy, selfcompassion, acceptance of others). There was a no correlation between total moral and total
interpersonal variables (empathy r = .183, n = 91, ns, 2-tailed), total acceptance of others
(r=.031, n= 98, ns, 2-tailed), self compassion (r =.076, n= 100, ns, 2-tailed).
Relationship between different Bullying Groups and Interpersonal Variables
Analysis of data, using an independent samples t-test found no statistically significant
difference between bully and non/bully group in relation to empathy (t = -1.865, df = 61.195,
ns, 2-tailed) indicating no statistical difference between bully and non-bully groups and
empathy. Bully group (M = 56.12, SD = 8.21, N=24) reported lower mean score of total
interpersonal variables than non-bully group (M = 60.30, SD= 12.56, N=71). Using a t-test
found no statistically significant difference between bully and non/bully group in relation to
acceptance of others (t = .055, df = 100, ns, 2-tailed) indicating no statistical difference
between nationality and Bully group (M = 49.28, SD = 7.03, N=25) reported lower mean
score of total interpersonal variables than non-bully group (M = 49.19, SD= 6.63, N=77). Ttest found no statistically significant difference between bully and non/bully group in relation
to self-compassion (t = -1.788, df =102, ns, 2-tailed) indicating no statistical difference
between bully and non bully group in relation to self-compassion. Bully group (M = 2.94, SD
= .546, N=25) reported lower mean score of total interpersonal variables than non-bully
group (M = 3.18, SD= .593, N=79). Using a t-test found no significant difference between
Traditional Vs Cyber-Bullying 34
cyberbully and non/cyberbully group and total interpersonal variables; empathy (t = -.771, df
= 95, ns, 2-tailed) indicating no statistical difference between cyberbullying groups and
empathy. Cyberbully group (M=56.83, SD=9.09, N=12) reported lower mean scores of total
empathy than non-cyberbully groups (M=59.60, SD=11.92, N=85). Using a T-test also
revealed no significant difference between cyberbully and non/cyberbully group and total
interpersonal variables; acceptance of other (t = .109, df = 102, ns, 2-tailed) indicating no
statistical difference between cyberbullying groups and acceptance of others. Cyberbully
group (M=49.38, SD=5.17, N=13) reported lower mean scores of total acceptance of others
than non-cyberbully groups (M=49.16, SD=6.98, N=91). Using a t-test found no significant
difference between cyberbully and non/cyberbully group and total interpersonal variables;
self-compassion (t = -.744, df = 104, ns, 2-tailed) indicating no statistical difference between
cyberbullying groups and self-compassion. Cyberbully group (M=2.99, SD=.660, N=12)
reported lower mean scores of total empathy than non-cyberbully groups (M=3.13, SD=.594,
N=94).
Relationship between Gender and Interpersonal Variables
Analysis of data using an independent sample t-test and found a significant difference
between gender in relation to empathy (t = -2.586, df = 91.193, p<.05, 2-tailed) indicating a
statistical difference between gender and empathy. Males (M = 56.11, SD = 8.74, N=44)
reported lower mean scores of empathy than females (M = 61.86, SD= 13.04, N=53). Using
t-test found no statistically significant difference between males and females in relation to
acceptance of others (t = -.898, df = 102, ns, 2-tailed) indicating no statistical difference
between gender and acceptance of others. Males (M = 48.51, SD = 7.57, N=45) reported
lower mean scores of acceptance of others than females (M = 49.71, SD= 6.06, N=59). T-test
found no statistically significant difference between males and females in relation to selfcompassion (t = -1.341, df = 104, ns, 2-tailed) indicating no statistical difference gender and
Traditional Vs Cyber-Bullying 35
self-compassion. Males (M = 3.02, SD =.539, N=45) reported lower mean scores of selfcompassion than females (M = 3.18, SD= .637, N=61).
Relationship between Gender and Moral Disengagement
Analysis of data, using an independent samples t-test found a significant difference
between males and females in relation to moral disengagement (t = -4.467, df = 99, p< .05, 2tailed) indicating a statistical difference between gender in relation to MD. Males (M = 91.38,
SD = 10.42, N=42) reported lower mean scores of total moral disengagement than females
(M = 100.98, SD= 10.80, N=59).
Relationship between Nationality and Moral Disengagement
Analysis of data, using an independent samples t-test found no statistically significant
difference between European and Asian nationals in relation to moral disengagement (t =
1.387, df = 99, ns, 2-tailed) indicating statistically no nationality difference in relation to MD
European (M = 98.15, SD = 11.72, N=66) reported a higher mean scores of total moral
disengagement than Asian nationals (M = 94.80, SD= 11.24, N=35).
Relationship between Nationality and Interpersonal Variables
An independent samples t-test found a statistically significant difference between
European and Asian in relation to empathy (t = -2.349, df = 95, p<.05, 2-tailed) indicating a
statistical difference between nationality and empathy. European (M = 57.26, SD = 10.49,
N=63) reported lower mean scores of empathy than Asian nationals (M = 62.94, SD= 12.79,
N=34). Using independent t-test revealed a significant difference between European and
Asians in relation to acceptance of others (t = 2.248, df = 102, p<.05, 2-tailed) indicating a
statistical difference between nationality and acceptance of others. European (M = 50.23, SD
= 6.80, N=69) reported lower mean scores of acceptance of others than Asian (M = 47.14,
SD= 6.23, N=35). Using independent t-test revealed no statistically significant difference
between European and Asian nationality in relation to self-compassion (t = -.352, df = 104,
Traditional Vs Cyber-Bullying 36
ns, 2-tailed) indicating a statistical difference between nationality and self-compassion.
European (M = 3.10, SD =.634, N=70) reported lower mean scores of self-compassion than
Asians nationals (M = 3.14, SD= .535, N=36).
Frequency and experiences of Bullying, Cyber-bullying, Victimization and online
activities
When participants were asked to report the frequency on demographics characteristics
of nationality & gender (see Appendix table 1A), current status (see Appendix table 1B) and
how much time spent online per week (see Appendix table 1C) are provided in detail (see
Appendix).
Gender and Nationality frequency with Activities Online (N=107)
Participants were asked to report on following question, “what activities do you use
the internet for?” Using a multiple response crosstab to show the total frequency and
percentages of gender, nationality and activities of online, it is provided in detail in (see
Appendix Table 2A, 2B).
Gender and Nationality frequency differences and the Places they Use the Internet (N=107)
All 107 participants responded to the multiple choice questions which asked “where is
the place you most like to use the Internet?” Using a multiple response crosstab to show the
total frequency and percentages of gender, nationality and the place you most like to use the
internet, it is provided in detail (see Appendix Table 3A, 3B).
Traditional Bullying & Cyber-bullying:
Frequency of knowing someone who was Bullied & Cyber-bullied
When participants (N=107) were asked to report on multiple response question “if
they knew anyone who was bullied face to face or cyber-bullied?” Using a multiple response
frequency table showed the total frequency and percentages participant and the place you
most like to use the internet, it is provided in detail (see Appendix Table 4A, 4B).
Traditional Vs Cyber-Bullying 37
Total Frequency differences of Gender, Nationality and Victimization
Participants were asked to report on if they were traditional victims or non-victim and
cyber-victims or non cyber-victims. They had to respond to the following question “have you
ever been bullied?” A multiple response crosstab was carried out between gender, traditional
victimization and cyber-victimization. Also results of nationality, traditional victimization
and cyber-victimization were found. Results provided in detail (see Appendix Table 5A, B,
C, D).
Total Overlap Frequency of Bullying, Cyber-bullying and Victimization
Using a multiple response crosstab found overlapping between bullying groups and
victims groups. Total frequency of victims and bullying groups (e.g. bully/victim & bully)
overlapping details provided (see Appendix Table 6A, B, C, D).
Total Frequency of how long ago were you a Traditional & Cyber-Victim?
Using a multiple response frequency table the results to the question, how long ago
where they bullied? Of the 107 participants majority of those victimized in traditional
bullying (N=23) reported that they were bullied over one working year ago. Also the most
majority (N=4) reported being cyber-bullied within the last week. Total frequency of how
long ago were you bullied are provided in detail (see Appendix Table 7A, B).
Did you tell anyone you were bullied or cyber-bullied?
A descriptive frequency table was used for the question did you tell anyone you were
bullied? Of the 107 participants who asked this in relation to being traditional bullied, the
majority (N=29) said yes I did tell someone, (N=23) said no, I was bullied but did not tell
anyone. In relation to being traditional bullied, the majority (N=8) said yes I did tell someone,
(N=11) said no, I was bullied but did not tell anyone. Using a crosstab analysis females
(N=14) would be most likely to tell someone compared to males (N=14) when traditional
Traditional Vs Cyber-Bullying 38
bullied, also females (N=6) were more likely to tell someone compared to the males (N=2)
when cyber-bullied. Using a multiple response crosstab (N=32) traditional victims would tell
someone they being bullied compared to (N=7) cyber-victims.
Total Frequency and the experiences of a Bully/Victim & Cyber-Victim
Using multiple response frequency table results from the experiences they had as a
cyber-victim. They were asked to report their experiences of being cyber-bullied and bullied.
Total frequency and percentages of experiences being victimized in bullying and cyberbullying is reported in detail (see Appendix Table 8A, 9B).
Total Frequency of how long ago were did you bully& cyber-bully others?
Using a frequency table reported the results to the question, how long ago did you
bully others? Of the 107 participants majority of those who bullied in traditional bullying
(N=9) reported that they bullied over one working year ago and (N=5) also reported bullying
over one working year ago. Total frequency of how long ago they bullied or cyber-bullied
others are provided in detail (see Appendix Table 9A, 9B).
Total Frequency experiences of bullying and cyber-bullying others
Using a multiple response frequency table to reported on their behaviours of being
bullies and cyber-bullies. Total frequency of behaviours of being bullies in cyber-bullying
reported in detail (see Appendix Table 10A, 10B).
Total Frequency of participant opinions if someone was bullied how would they feel?
Using a multiple response frequency table to analysis opinions to the following
question; if someone was bullied, how would they feel? How someone who is cyber-bullied or
traditional bullied would feel is reported in detail in (see Appendix Table 11A, 11B).
Traditional Vs Cyber-Bullying 39
Total Frequency of Gender, Nationality and the opinions on; what do you think are the best
ways to stop ‘Traditional’ Bullying & Cyber-Bullying?
Using a multiple response crosstab looked at the differences between frequency of
gender, nationality, and their opinions on how to stop bullying behaviour. When asked; what
do you think are the best ways to stop ‘traditional’ bullying? Gender & nationality
differences on how someone who is traditional bullied would feel is reported in detail (see
Appendix Table 12 A, B, C, D).
Total Frequency of the opinions of participants weather Cyber-bullying is more harmful than
Bullying
Using a multiple response frequency the opinions of participant was sought on cyberbullying and how harmful it is in comparisons to traditional bullying. They were asked to
report their opinions to the following cyber-bullying methods and weather they are less, same
or more harmful than traditional bullying. Total frequency of their opinions on weather
cyber-bullying is more harmful than traditional bullying reported in their categories, less
harmful, same and more harmful, results is reported in detail (see Appendix Table 13 A, B,
C, D).
Relationship between Bullying and Cyber-bullying groups and time spent online
Analysis of data, using an independent samples t-test found a statistically significant
difference between bully and non/bully group in relation time spent online (t =-2.456, df =
103, p<.05, 2-tailed) indicating a significant difference between bully and non-bully and time
spent online. Bully (M = 2.68, SD = 1.54, N=25) reported a lower mean scores on hours
online per week than non-bully group (M = 3.51, SD= 1.45, N=80). Using an independent
samples t-test was conducted to examine whether there was a significant difference between
bullying groups in relation to time spent online. The test revealed no statistically significant
Traditional Vs Cyber-Bullying 40
difference between cyber-bullying and non-cyberbullying group in relation to hours online
per week (t =-.963, df = 17.80, ns, 2-tailed) indicating no significant difference between
cyber-bullies and non-cyberbullies and time spent online. Non-cyberbullies (M = 3.36, SD =
1.55, N=94) reported a higher mean scores of hours spent online per week than cyberbullying group (M = 3, SD= 1.22, N=13).
Relationship between Nationality and Bullying/Cyber-bullying Groups
Using Chi square test it was found there is no significant difference between
European, Asian nationals and bully/non-bully groups (Pearson chi square χ² (1, N = 105) =
3.339, ns, Phi = .178). Using chi square test it was found there is no significant difference
between European, Asian nationals and cyber-bully/non-cyberbully groups (Pearson chi
square see χ² (1, N = 105) = .865, ns, Phi = .090).
Relationship between Cyber-bullying and time spent online
Two-way contingency table analyses examining differences between cyber-bully and
non-cyberbully groups in relation to time spent online. Chi square test found a significant
association between cyber-bully and non-cyber/bully groups (Pearson chi square χ² (4, N =
107) = 9.130, p <.05, V = .292) indicating that cyber-bullying groups was significantly
associated with time spent online. Time spent online (V=.307) had a small effect on taking
part in bullying (Cyber-Bully, Non Cyber-bully Groups) Non-Cyberbullies reported higher
frequency of time spent online compared to Cyberbullies. For detailed analysis of frequency
see (Figure 3).
Traditional Vs Cyber-Bullying 41
Figure 3. 2x2 contingency table analysis
Cyber-bullying , non-cyberbullying & time spent online
Have you ever taken part in
cyberbullying
yes
On average, how long do you 0-5 hours
spend on the internet per
no
Total
0
15
15
5-10 hours
6
19
25
10-15 hours
4
14
18
15-20 hours
0
9
9
20 or more hours
3
37
40
13
94
107
week?
Total
Frequency Relationship between Cyber-victims and time spent online
Using multiple response crosstab to examine differences between cybervictims and non
cyberbully in relation to time spent online. The table shows that the more time spent online
20 or more hours the more cybervictims (N=6). For detailed analysis of frequency see
(Figure 4).
Traditional Vs Cyber-Bullying 42
Figure 4. Multiple Cross tab table analysis
Cyber-victims and time spent online
On average, how long do you spend on the internet
per week?
20 or
0-5
5-10
10-15
15-20
more
hours
hours
hours
hours
hours
Total
No
Count
13
23
13
8
34
91
Yes, Inside school
Count
0
0
1
0
1
2
Yes, Outside school Count
1
2
2
0
3
8
Yes, Inside work
Count
1
0
0
0
0
1
Yes, Outside work
Count
1
0
2
0
0
3
Both inside and
Count
0
0
1
0
2
3
Count
15
25
18
8
40
106
outside school
Total
Percentages and totals are based on respondents.
Note. Missing Value (N=1)
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 43
Discussion
In light of the vast amount of academic research on the subject area of face-to-face
bullying, now a different form of peer maltreatment has emerged, that of cyber-bullying. This
study provides further evidence that in addition to the age-old problem of face-to-face
bullying, this research was conduct to uncover the nature of cyber-bullying, (a topic which is
drought of research) and to distinguish it from traditional bullying. Of particular interest
within these goals our first aim was examine some frequencies of traditional and cyberbullying.
A second aim was to examine the differences between gender, nationality and
bullying (bully/non-bully) and cyber-bully (cyber-bully/non cyber-bully) groups. Recent
studies have also demonstrated that there is an overlap between types of bullying groups
(Smith et al., 2008; Ybarra et al., 2004). This current research investigated this overlap
between bully/non-bully and cyber-bully/non-cyberbully groups and it was expected that
Traditional bullying groups and cyber-bullying groups to overlapped each other. Also Bully
groups and the overlap of victimization groups was observed and reported.
This study’s third aim sought to examine the association between cognitive
mechanisms (MD) applied by people, in order to rationalize and justify harmful acts of
traditional and cyber-bullying. A unique characteristic of cyber-bullying is anonymity or the
ability to hide ones identity online behind technologies, putting a distance between the
perpetrator and the victim. Li (2005) found 40% of victims did not know the identity of their
online perpetrator and the perpetrator will not be able to observe the victims reactions or the
consequences of their actions. This anonymity online could be a contributing factor of moral
disengagement. The concept of moral disengagement (Bandura, 1975, 1996, 1999) provided
an appropriate theoretical framework for achieving this aim. We wanted to investigate the
Traditional Vs Cyber-Bullying 44
relationship between the overall levels of MD, traditional, cyber-bullying groups and
observing gender and nationality differences.
A fourth aim of this was to investigate if moral disengagement was associated to
interpersonal variables. Additionally, we sought to exam if overall levels of interpersonal
variables (empathy, self-compassion and acceptance of others) and traditional and cyberbullying groups differed. Finally interpersonal variables, gender and nationality differences
were observed.
Frequencies of internet use
It was found of the total 107 participants, 100% indicated that they use the internet.
The majority of participants spent 20 or more hours online per week. Asian participants, they
spent the most amount of time online per week (20+ hours) compared to the majority of
Europeans spending only 5-10 hours online per week. Smith et al (2008) found no significant
difference with frequency of internet use and people engaging in bullying behaviours. Results
from the current study indicated there was a significant difference between bully and nonbully groups and time spent online per week but not for cyber-bullying groups. Non
traditional bullies spent more time online than traditional bully groups but no significant
difference was found between the cyber-bullying groups and time spent online per week. This
suggests that people spending more time online seems to be not face-to-face bullies. Hinduja
et al. (2008) found that the more time respondents spent online and the more computers
proficient they are, the more likely they experienced cyberbullying. The current findings
supported this, the majority of cyber-victims spent the most time online per week. The
majority of cyber-victims in this study spent 20 hours or more online per week.
Telling someone when they where bullied & cyber-bullied
When participants were asked “did you tell anyone they were being bullied or cyberbullied”? Results found that the majority of participants (N=29) of those said “yes I did tell
Traditional Vs Cyber-Bullying 45
someone” and (N=23) said no “I was bullied but did not tell anyone I was being bullied faceto-face” comparing this to only (N=8) of those being cyber-bullied told someone and (N=11)
said “no, I was being cyber-bullied but did not tell anyone”. Results found that females
where more incline to tell someone they were being traditional bullied and cyber-bullied
compared to boys. These current findings supports previous findings, that female who are
cyber-bully victims were more likely to inform adults than their male counterparts (Li, 2005).
Also a higher majority of the participants would tell someone when they are bully/victims
compared to cyber-victims. Higher percentages of telling someone they got bullied face-toface might be because they know who their perpetrator was and can direct authorities to help
intervene.
How long ago were did you bully or cyber-bully others?
Of the 107 participants majority of those who bullied in traditional bullying reported
that they bullied over one working year ago. In relation to being a cyber-bully the majority
also reported bullying over one working year ago. This may be due to the participant mean
age of 25 years old. The older participants are the less bullying activity they will engage in.
Scheithauer et al. (2006) results confirm this, there findings indicated a decrease in bullying
activity with the increase in age.
Overlap of Bullying groups and Victimization groups
A reason for why males in this study were found to be the perpetrator of bullying and
cyber-bullying could be because of the overlapping of bullying groups. Recent studies have
also demonstrated that there is an overlap between types of bullying such as that cybervictims were traditional victims, many cyber-bullies were traditional bullies (Smith et al.,
2008; Ybarra et al., 2004). As predicted results found that from 100% of participants reported
the following overlap of bullying groups; bully/cyberbully, non-bully/non-cyberbully, non-
Traditional Vs Cyber-Bullying 46
bully/cyberbully, non/bully and non-cyberbully. Indicating that bully/non-bully was
significantly related to cyberbully/non-cyberbullying groups and taking part in bullying had a
moderate effect on you taking part in cyber-bullying. These results support the first
hypothesis (H1), that there is overlapping between traditional and cyber-bullying groups.
Results also found that overall cyber-bullies and non-cyberbullies showed an overlap
between cyber-victim, non-cybervictim, bully/victims and non-bully victims. Also bullies and
non bullies showed an overlap between cyber-victim, non-cybervictim, bully victims and
non-bully victims. Reasons for this overlapping of bullying groups, could be in retaliation or
an act or revenge. Shariff, (2008) found both traditional bullying and cyber-bullying have the
same basic motivation: retaliation (revenge for having been violated) and the perpetrator's
desire for power and control.
Gender, Nationality and their Relationship to Traditional & Cyber-bullying Groups
Results showed as predicted support for the first part of the second hypothesis (H2)
that males and females were both involved in bullying and cyber-bullying groups, there was a
significant difference between males and females and bully and non-bully groups. More
males were in the traditional bullies than females and more females reported being nonbullies compared to the males. Gender had a moderate effect on being in a bully or non-bully
group in traditional bullying. These findings are in line with previous research, with
(Bjorkqvist, 1992) findings, that girls overall make greater use of indirect means of
aggression, whereas boys tend to employ direct means. No significant difference was found
between males and females and cyber-bully/non-cyberbully groups. Thus the second part of
H2 is not supported fully and must be rejected and Null hypothesis is accepted. More males
reported being cyber-bullies than females and more females reported being a non-cyberbully
compared to the males. Gender had no effect on being in a cyber-bully or non-cyberbully
group in cyber-bullying. Reasons for this may be that males are more involved in both forms
Traditional Vs Cyber-Bullying 47
of bullying behaviour than the females. Although it was not significant there was only N=1 in
the difference between males and females and cyber-bullying group. Females where near to
equal in the cyber-bully group as the males. These results were close to the results of Ybarra
et al. (2004), according to the findings of the study, it was found in a United States sample,
males and females were equally likely to report harassing another person online. Results also
determined that there was no significant difference between European and Asian nationals
and Bully and non-Bully groups and also no significant difference between the Nationalities
and cyber-bully and non-cyberbully groups. Indicating that being of a particular nationality
did not effect if you were going to be a bully or non-bully or cyber-bully/non-cyberbully.
More Europeans were in the bully group than Asians and more Europeans reported being a
non-bully compared to the Asian nationals. More Europeans reported being in a cyber-bully
group than Asian nationals and more Europeans reported being a non-cyberbully compared to
the Asian. This is most likely down to the fact that 65.4% of the sample was of European
nationality compare to 34.6% Asian nationals. Results also found that more females than
males where cyber-victims and bully victims. This supports previous research that, females
were more likely to be victims in cyber-bullying compared to males (Smith, et al. 2008; Li,
2005). These current findings support similar findings in (Dilmac, 2009) study which
reported girls to be classified as bully/victims.
Note: Moral disengagement (please note: lower means scores on MD indicate more moral
disengagement)
Moral disengagement, Bully groups, Gender and Nationality
Results found no significant difference between bullying groups and MD, rejecting
the H3, the null hypothesis is supported. However the mean scores did show differences
between bullying groups and MD. Cyber-bully group scored lower levels on total MD than
the traditional bully group. This suggests that Cyber-bullies who engage in bullying
Traditional Vs Cyber-Bullying 48
behaviour are characterized by more distorted thought patterns, which support aggressive
behaviour. They make more justifications and rationalizations in order to make a harmful act
seem less harmful and to eliminate self-censure. These findings are in line with earlier
studies, which found high levels of MD in generally, was associated with aggression and
bullying, and that MD was a common place in both bullying and cyber-bullying (Turner et al.
2007, Pornari et al, 2010). Our findings of mean scores confirm this also, that the Non
bullying groups scored higher means on MD than the aggressive roles, or bullying groups.
MD may be higher in the cyber-bullying group because of anonymity or the ability to hide
ones identity online behind technologies, putting a distance between the perpetrator and the
victim. Li (2005) found that out of 177 Canadian students over 40% did not know the identity
of their online perpetrator and the perpetrator will not be able to observe the victims reactions
or the consequences of their actions. Results also found a significant difference between MD
and gender. That males scored lower on MD than females indicating that male’s moral
disengaged more which is in line with previous research of (Gini 2006) who found that MD
was associated to all aggressive roles in bullying and in the current study males were the
majority in both bully and cyber-bully groups.
Moral Disengagement and Interpersonal Variables and Bullying groups
Results found no significant relationship between MD and interpersonal Variables.
H4 is rejected and null hypothesis is accepted. This may suggest that people who are morally
disengaged (bully groups) would be less likely to be empathetic, self-compassionate or
acceptant of others than the non bully groups who were less morally disengage.
Results found that there was no significant difference between Interpersonal Variables
and bullying groups. H5 is therefore rejected and the null hypothesis is accepted, indicating
no bully group difference in relation to interpersonal variables. However Bullying groups
(bully, cyber-bully) reported lower mean scores in empathy, self-compassion and acceptance
Traditional Vs Cyber-Bullying 49
of others than the non-bully groups (non-bully, non-cyberbully). The reason for this could be
in line with previous research, (Turner et al 2007) found that moral disengagement was
negatively correlated with pro-social behaviours. Empathy has been negatively linked to
bullying behaviour or bully groups compared to non-bullying groups or behaviour (Caravita
et al. 2009, Jollife, 2006, Steffgen, 2009). In this current research results showed there was a
higher number of non-bullies than bullies thus the acceptance of others score maybe low in
bullying groups compare to non-bully groups because bullies might want to be accepted by
other bullies rather than non-bullies which would be in line with previous research, Olthof,
(2008) result found that children’s bullying behaviour would be positively related to their
desire to be accepted by other bullying children, while being unrelated or negatively related
to their desire to be accepted by non-bullying children. Self compassion scores maybe low in
bullying groups because as Neff, (2003) highlighted in individual and group differences of
self-compassion that the ability to recognize and attend to internal feeling states is linked to
the empathy that children receive from their caregivers early on. This suggests that
individuals who experienced warm, supportive relationships with their parents as children,
and who perceived their parents as understanding and compassionate, should tend to have
more self-compassion as adults.
Results also found that traditional bullies scored lower empathy, self compassion and
acceptance of others than cyber-bullies, but there was no significant difference between
bullying groups and interpersonal variables thus the H6 is rejected and the null hypothesis is
accepted. It was found that bullies scored lower on the interpersonal variables than cyberbullies. This might suggest that face-to-face bullying requires lower levels of interpersonal
variables because you can see the damaging effect your actions cause on your victims.
The results revealed as predicted a significant difference between males and females
in relation to empathy. Males reported lower empathy than females. This supports part of H7.
Traditional Vs Cyber-Bullying 50
The result revealed no statistically significant difference between males and females in
relation to acceptance of others. Rejecting the second part of H7 and accepting the null
hypothesis. Males reported lower of acceptance of others than females. Rest also revealed no
statistically significant difference between males and females in relation to self-compassion.
Rejecting the third part to H7 and accepting the null hypothesis. Males reported less selfcompassion than females. These results supported (Ang et al, 2010) study that found that girls
had higher affective empathy. Boys and girl bullies were significantly associated with low
levels of empathtic responsiveness (Steffgen, 2009).
Moral disengagement, Interpersonal variables and Nationality differences
The results also revealed no statistically significant difference between European and
Asian nationals in relation to moral disengagement. However European reported a higher
mean scores of total moral disengagement than Asian nationals which is interesting because
the majority of participants where Europeans. Results found there was a significant difference
between European and Asian nationals in relation to empathy. European reported lower mean
scores of empathy than Asian nationals. The test revealed a significant difference between
European and Asians in relation to acceptance of others. Results found that Europeans
reported lower acceptance of others than Asian. There was no statistically significant
difference between European and Asian nationality in relation to self-compassion. Europeans
nationals reported lower mean scores of self-compassion than Asians nationals. Reasons that
Asians scored higher on interpersonal variables compared to Europeans might be because
they maybe consider others rather than themselves. Pakers et al. (1999) results found that the
Asian sample being from a collectivist society gave abstract social concept of self, like an
“ordinary citizen”, “a human like others”. This concept of person was completely opposite to
the individualistic western sample indicating the self as “unique”, “special and different from
others”. In particular, cultural values on the self, thinking patterns, emotional expression,
Traditional Vs Cyber-Bullying 51
conflict resolution, social harmony, and virtues may influence the processes of social
interactions, including interpersonal variables like empathy self compassion. . Ho, et al,
(2011) “Individualism is a social pattern that involves individuals’ perceptions of themselves
as relatively independent of others; emphasizes individual preferences, needs, and rights;
gives priority to personal goals over group goals; and encourages rational cost– benefit
analyses of social relationships and contractual relationships. In contrast, collectivism is a
social pattern consisting of closely linked individuals viewing themselves as interdependent
with others; emphasizes social norms, obligations, and duties; and values social
connectedness and social harmony. A review indicated that several features of a collectivistic
worldview such as societal pressure to maintain social harmony and minimize conflict”
(p.77-78).
Limitations:
1. Measures used in this study were all self-report.
2. One of the most important limitations of the current study is the problem that
has also been addressed in previous research in this area, that of not having a
standardized operational definition for cyber-bullying and victimization or a
standardized method of measuring this phenomenon. Studies vary widely in
the types and number of questions used to assess cyber bullying and
victimization as well as frequency (e.g., the number of times it occurred and
the time frame in which it was assessed).
3. Sample size was quiet small and might be hard to generalize, also if there were
more Asian nationals informants it might have elucidate the current findings.
4. Language of questionnaire, Asian participants may have had difficulty in
understanding some of the questions or maybe miss understood the definition
of bullying and cyber-bullying.
Traditional Vs Cyber-Bullying 52
Future directions
Scheithauer et al. (2006), findings indicated a decrease in bullying activity with the increase
in age. Future research should also consider using younger age groups in its sample.
Although this was certainly not the case with the sample used in this study, perhaps different
frequencies, trends, and characteristics exist for younger children than for adults. This study
likewise suffered from an unequal representation of participants of Asian nationalities
compared to Europeans. Perhaps more differences could have been uncovered if the Asian
sample were more fully represented. Future research should aim to adequately sample
younger participants to confirm the present findings. Also MD mean scores seemed highest
within bullying Cyber-Bullying group, although it was no significant difference between MD
and bullying groups. Investigating MD, bullying group and a younger sample may produce
more significant findings. Further research into cyber-bullying is needed. Motivations behind
cyber-bullying should be researched in the future, as this area is non-existent in relation to
cyber-bullying as fair as I am aware.
Traditional Vs Cyber-Bullying 53
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Traditional Vs Cyber-Bullying 60
Appendix
Table 1A: Participants Characterised by Nationality and Gender (N=107)
Nationality
Male
Female
Overall Total
European
34
36
70
Asian
11
26
37
Total
45
62
107
Note. Total N=107.
Table 1B: Participants Characterised by Nationality and Current status (N=105)
Current status
European
Asian
Overall Total
Working
22
15
40
Attending
University/College
26
19
45
Both
17
3
20
Total
68
37
105
Note. Missing value N=2.
Table 1C: Participants Characterised by Nationality and time spent online per week
(N=107)
Time spent online
per week
European
Asian
Overall Total
0-5 hours
11
4
15
5-10 hours
20
5
25
10-15 hours
15
3
18
15-20 hours
6
3
9
20 or more hours
18
22
40
Total
70
37
107
Note. N=107.
Traditional Vs Cyber-Bullying 61
Table 2A
Gender and Activities online
Male/Female
Activities Online
Surfing the net
male
Count
% of Total
Chat rooms
Count
% of Total
To send or receive emails
Count
% of Total
Instant Messaging i.e.
Msn/Skype
Schoolwork
Count
% of Total
Count
% of Total
Downloading music, movies
or programs
Playing games
Count
% of Total
Count
% of Total
Online Shopping
Count
% of Total
Other
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.
female
Total
37
53
90
34.6%
49.5%
84.1%
10
11
21
9.3%
10.3%
19.6%
34
49
83
31.8%
45.8%
77.6%
19
23
42
17.8%
21.5%
39.3%
19
34
53
17.8%
31.8%
49.5%
29
39
68
27.1%
36.4%
63.6%
14
15
29
13.1%
14.0%
27.1%
20
33
53
18.7%
30.8%
49.5%
2
7
9
1.9%
6.5%
8.4%
45
62
107
42.1%
57.9%
100.0%
Traditional Vs Cyber-Bullying 62
Table 2B
Nationality and online activities
European/Asian?
Activities online
Surfing the net
European
Count
% of Total
Chat rooms
Count
% of Total
To send or receive emails
Count
% of Total
Instant Messaging i.e.
Msn/Skype
Schoolwork
Count
% of Total
Count
% of Total
Downloading music, movies
or programs
Playing games
Count
% of Total
Count
% of Total
Online Shopping
Count
% of Total
Other
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.
Asian
Total
57
33
90
53.3%
30.8%
84.1%
10
11
21
9.3%
10.3%
19.6%
55
28
83
51.4%
26.2%
77.6%
23
19
42
21.5%
17.8%
39.3%
36
17
53
33.6%
15.9%
49.5%
41
27
68
38.3%
25.2%
63.6%
16
13
29
15.0%
12.1%
27.1%
37
16
53
34.6%
15.0%
49.5%
8
1
9
7.5%
.9%
8.4%
70
37
107
65.4%
34.6%
100.0%
Traditional Vs Cyber-Bullying 63
Table 3A
Gender & place you like to use the internet
Male/Female
Place of internet use
I do not use the internet
male
Count
% of Total
In my bedroom
Count
% of Total
At home, not in bedroom
Count
% of Total
At School
Count
% of Total
Friend's House
Count
% of Total
Work
Count
% of Total
At a local library
Count
% of Total
Internet Cafe
Count
% of Total
At a relative’s house
Count
% of Total
Other
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
Note. Missing value (N=5)
a. Dichotomy group tabulated at value 1.
female
Total
3
0
3
2.9%
.0%
2.9%
27
43
70
26.5%
42.2%
68.6%
23
22
45
22.5%
21.6%
44.1%
13
18
31
12.7%
17.6%
30.4%
8
7
15
7.8%
6.9%
14.7%
12
19
31
11.8%
18.6%
30.4%
7
10
17
6.9%
9.8%
16.7%
5
10
15
4.9%
9.8%
14.7%
5
4
9
4.9%
3.9%
8.8%
6
4
10
5.9%
3.9%
9.8%
45
57
102
44.1%
55.9%
100.0%
Traditional Vs Cyber-Bullying 64
Table 3B
Nationality & place you like to use the internet
European/Asian?
Place of internet use
I do not use the internet
European
Count
% of Total
In my bedroom
Count
% of Total
At home, not in bedroom
Count
% of Total
At School
Count
% of Total
Friend's House
Count
% of Total
Work
Count
% of Total
At a local libarary
Count
% of Total
Internet Cafe
Count
% of Total
At a relatives house
Count
% of Total
Other
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
Note. Missing value (N=5)
a. Dichotomy group tabulated at value 1.
Asian
Total
2
1
3
2.0%
1.0%
2.9%
42
28
70
41.2%
27.5%
68.6%
34
11
45
33.3%
10.8%
44.1%
23
8
31
22.5%
7.8%
30.4%
10
5
15
9.8%
4.9%
14.7%
23
8
31
22.5%
7.8%
30.4%
13
4
17
12.7%
3.9%
16.7%
11
4
15
10.8%
3.9%
14.7%
7
2
9
6.9%
2.0%
8.8%
7
3
10
6.9%
2.9%
9.8%
67
35
102
65.7%
34.3%
100.0%
Traditional Vs Cyber-Bullying 65
Table 4A
Total Responses to knowing someone who was bullied
Responses
Knowing someone
Bullied
N
Percent
Percent of Cases
No
28
18.4%
26.2%
Yes, Inside school
40
26.3%
37.4%
Yes, Outside school
13
8.6%
12.1%
Yes, Inside work
18
11.8%
16.8%
Yes, outside work
4
2.6%
3.7%
32
21.1%
29.9%
17
11.2%
15.9%
152
100.0%
142.1%
Both inside and outside
school
Both inside and outside work
Total
Note. Participant (N) may exceed (N=107) due to the ability to give multiple responses to this
question.
a. Dichotomy group tabulated at value 1
Table 4B
Total Responses to knowing someone who was Cyber-bullied
Responses
Knowing someone
Cyber-bullied
N
Percent
Percent of Cases
No
68
59.1%
64.8%
Yes, Inside school
10
8.7%
9.5%
Yes, Outside school
9
7.8%
8.6%
Yes, outside work
6
5.2%
5.7%
15
13.0%
14.3%
7
6.1%
6.7%
115
100.0%
109.5%
Both inside and outside
school
Both inside and outside work
Total
Traditional Vs Cyber-Bullying 66
Table 5A
Gender & Traditional Bully-Victims
Male/Female
Bully/Victim
No
male
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Both insidhe and outside
work
Total
Count
% of Total
Count
% of Total
Count
% of Total
female
Total
23
33
56
21.5%
30.8%
52.3%
16
16
32
15.0%
15.0%
29.9%
2
5
7
1.9%
4.7%
6.5%
1
5
6
.9%
4.7%
5.6%
0
2
2
.0%
1.9%
1.9%
5
5
10
4.7%
4.7%
9.3%
3
1
4
2.8%
.9%
3.7%
45
62
107
42.1%
57.9%
100.0%
Percentages and totals are based on respondents.
Note. Participant (N) may exceed (N=107) due to the ability to give multiple responses to this
question.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 67
Table 5B
Gender & Cyber-Victims
Male/Female
Cyber-Victim
No
male
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Total
Count
% of Total
Count
% of Total
female
Total
37
54
91
34.9%
50.9%
85.8%
1
1
2
.9%
.9%
1.9%
4
4
8
3.8%
3.8%
7.5%
0
1
1
.0%
.9%
.9%
1
2
3
.9%
1.9%
2.8%
2
1
3
1.9%
.9%
2.8%
44
62
106
41.5%
58.5%
100.0%
Percentages and totals are based on respondents.
Note. Missing Value (N=1). Participant (N) may exceed (N=107) due to the ability to give multiple
responses to this question.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 68
Table 5C
Nationality & Traditional Bully-Victims
European/Asian?
Bully/Victim
No
European
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Both insidhe and outside
work
Total
Count
% of Total
Count
% of Total
Count
% of Total
Asian
Total
31
25
56
29.0%
23.4%
52.3%
26
6
32
24.3%
5.6%
29.9%
6
1
7
5.6%
.9%
6.5%
6
0
6
5.6%
.0%
5.6%
1
1
2
.9%
.9%
1.9%
6
4
10
5.6%
3.7%
9.3%
3
1
4
2.8%
.9%
3.7%
70
37
107
65.4%
34.6%
100.0%
Percentages and totals are based on respondents.
Note. Participant (N) may exceed (N=107) due to the ability to give multiple responses to this
question.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 69
Table 5D
Nationality & Cyber-Victims
European/Asian?
Cyber-Vitim
No
European
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Total
Count
% of Total
Count
% of Total
Asian
Total
59
32
91
55.7%
30.2%
85.8%
1
1
2
.9%
.9%
1.9%
5
3
8
4.7%
2.8%
7.5%
1
0
1
.9%
.0%
.9%
3
0
3
2.8%
.0%
2.8%
2
1
3
1.9%
.9%
2.8%
69
37
106
65.1%
34.9%
100.0%
Percentages and totals are based on respondents.
Note. Missing Value (N=1). Participant (N) may exceed (N=107) due to the ability to give multiple
responses to this question.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 70
Table 6A
Overlapping of Bully/Victim & Bully, Non-Bully
Have you ever taken part in
bullying
Bully/Victim
No
yes
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Both insidhe and outside
work
Total
Count
% of Total
Count
% of Total
Count
% of Total
no
Total
8
48
56
7.6%
45.7%
53.3%
11
19
30
10.5%
18.1%
28.6%
2
5
7
1.9%
4.8%
6.7%
1
5
6
1.0%
4.8%
5.7%
0
2
2
.0%
1.9%
1.9%
4
6
10
3.8%
5.7%
9.5%
3
1
4
2.9%
1.0%
3.8%
25
80
105
23.8%
76.2%
100.0%
Percentages and totals are based on respondents.
Note. Missing Value (N=2). Participant (N) may exceed (N=107) due to the ability to give multiple responses
to this question.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 71
Table 6B
Overlapping of Bully/Victim & Cyber-Bully, Non-CyberBully
Have you ever taken part in
cyberbullying
Bully/Victim
No
yes
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Both insidhe and outside
work
Total
Count
% of Total
Count
% of Total
Count
% of Total
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.
no
Total
5
51
56
4.7%
47.7%
52.3%
5
27
32
4.7%
25.2%
29.9%
1
6
7
.9%
5.6%
6.5%
0
6
6
.0%
5.6%
5.6%
0
2
2
.0%
1.9%
1.9%
3
7
10
2.8%
6.5%
9.3%
1
3
4
.9%
2.8%
3.7%
13
94
107
12.1%
87.9%
100.0%
Traditional Vs Cyber-Bullying 72
Table 6C
Overlapping of Cyber-Victim & Bully, Non-Bully
Have you ever taken part in
bullying
CyberVictim
No
yes
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Total
Count
% of Total
Count
% of Total
no
Total
21
69
90
20.2%
66.3%
86.5%
1
1
2
1.0%
1.0%
1.9%
2
6
8
1.9%
5.8%
7.7%
1
0
1
1.0%
.0%
1.0%
1
2
3
1.0%
1.9%
2.9%
0
2
2
.0%
1.9%
1.9%
25
79
104
24.0%
76.0%
100.0%
Percentages and totals are based on respondents.
Note. Missing Value (N=3). Participant (N) may exceed (N=107) due to the ability to give multiple responses
to this question.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 73
Table 6D
Overlapping of Cyber-Victim & Cyber-Bully, Non-Cyber-Bully
Have you ever taken part in
cyberbullying
CyberVictim
No
yes
Count
% of Total
Yes, Inside school
Count
% of Total
Yes, Outside school
Count
% of Total
Yes, Inside work
Count
% of Total
Yes, Outside work
Count
% of Total
Both inside and outside
school
Total
Count
% of Total
Count
% of Total
no
Total
8
83
91
7.5%
78.3%
85.8%
1
1
2
.9%
.9%
1.9%
3
5
8
2.8%
4.7%
7.5%
0
1
1
.0%
.9%
.9%
2
1
3
1.9%
.9%
2.8%
0
3
3
.0%
2.8%
2.8%
13
93
106
12.3%
87.7%
100.0%
Percentages and totals are based on respondents.
Note. Missing Value (N=1). Participant (N) may exceed (N=107) due to the ability to give multiple responses
to this question.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 74
Table 7A
How long ago were you bullied?
Responses
How long ago bullied?
Never
N
Percent
Percent of Cases
53
49.5%
51.5%
Within the last week
4
3.7%
3.9%
Within the last month
1
.9%
1.0%
This term
1
.9%
1.0%
Within the last school year
2
1.9%
1.9%
Over one school year ago
20
18.7%
19.4%
Within the last working year
3
2.8%
2.9%
Over one working year ago
23
21.5%
22.3%
107
100.0%
103.9%
Total
a. Dichotomy group tabulated at value 1.
Table 7B
How long ago were you Cyber-bullied?
Responses
How long ago Cyberbullied?
Never
N
Percent
Percent of Cases
90
84.1%
84.9%
Within the last week
4
3.7%
3.8%
Within the last month
1
.9%
.9%
This term
2
1.9%
1.9%
Within the last school term
2
1.9%
1.9%
Over one school year ago
4
3.7%
3.8%
Within the last working year
1
.9%
.9%
Over one working year ago
3
2.8%
2.8%
107
100.0%
100.9%
Total
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 75
Table 8A
As a Bully/Victim, what experiences did you have?
Responses
Bully/Victim
experiences
N
Percent
Percent of Cases
I have never been bullied
51
20.0%
48.1%
Punched, kicked or
14
5.5%
13.2%
Damaged/ stolen belongings
12
4.7%
11.3%
Called names
37
14.5%
34.9%
Teased
40
15.7%
37.7%
Threatened
16
6.3%
15.1%
Being left out or excluded
28
11.0%
26.4%
Had rumours spread about
28
11.0%
26.4%
Because of my race/colour
6
2.4%
5.7%
Because of an illness or
2
.8%
1.9%
8
3.1%
7.5%
11
4.3%
10.4%
2
.8%
1.9%
255
100.0%
240.6%
psyshically hurt
me
disability
Because of my religion
Being called "GAY" even tho
it is not true
Other
Total
a. Dichotomy group tabulated at value 1. Note. Participant (N) may exceed (N=107) due to the ability
to give multiple responses to this question.
Traditional Vs Cyber-Bullying 76
Table 8B
As a Cyber-Victim, what experiences did you have?
Responses
Cyber-Victim
experiences
I have never been
N
Percent
Percent of Cases
86
72.3%
82.7%
5
4.2%
4.8%
Prank or silent phone calls
9
7.6%
8.7%
Through rude or nasty emails
4
3.4%
3.8%
11
9.2%
10.6%
2
1.7%
1.9%
2
1.7%
1.9%
119
100.0%
114.4%
cyberbullied
Threats through text
messages(making threats,
comments)
Insults on a website
Insults on Instant Messaging
MSN Messenger/AOL/Yahoo
In a chat room
Total
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 77
Table 9A
How long ago did you Bully someone?
Responses
How long ago did you
Bully someone?
Never
N
Percent
Percent of Cases
79
74.5%
76.0%
Within the last month
1
.9%
1.0%
Within the last school term
4
3.8%
3.8%
Within the last working year
4
3.8%
3.8%
Over one school year ago
2
1.9%
1.9%
Over one working year ago
9
8.5%
8.7%
Other
7
6.6%
6.7%
106
100.0%
101.9%
Total
a. Dichotomy group tabulated at value 1. Note. Missing Value (N=1).
Table 9B
How long ago did you Cyber-Bully someone?
Responses
How long ago did you
Cyber-bully?
Never
N
Percent
Percent of Cases
93
86.9%
87.7%
Within the last week
1
.9%
.9%
Within the last school term
3
2.8%
2.8%
Within the last working year
1
.9%
.9%
Over one shcool year ago
1
.9%
.9%
Over one working year ago
5
4.7%
4.7%
Other
3
2.8%
2.8%
107
100.0%
100.9%
Total
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 78
Table 10A
What behaviour did you engage in as a Bully?
Responses
Bully Behaviour
I have never taken part in
N
Percent
Percent of Cases
78
52.7%
73.6%
3
2.0%
2.8%
2
1.4%
1.9%
Calling somone names
13
8.8%
12.3%
Teasing
19
12.8%
17.9%
4
2.7%
3.8%
10
6.8%
9.4%
Sprading rumours
5
3.4%
4.7%
Bullied someone because
3
2.0%
2.8%
1
.7%
.9%
1
.7%
.9%
6
4.1%
5.7%
3
2.0%
2.8%
148
100.0%
139.6%
bullying
Punching, kicking or
physically hurting another
Damaging/stealing
belongings
Threatening
Leaving someone out or
excluding them
had an illness/disability
Bullied someone because
they had an illness/disability
Bullied someone because of
their religion
Called someone gay even
tho it is not true
Other
Total
a. Dichotomy group tabulated at value 1. Note. Participant (N) may exceed (N=107) due to the
ability to give multiple responses to this question.
Traditional Vs Cyber-Bullying 79
Table 10B
What behaviour did you engage in as a Cyber-Bully?
Responses
Cyber-bully behaviour
I have never taken part in
N
Percent
Percent of Cases
95
81.9%
88.8%
2
1.7%
1.9%
Prank or silent phone calls
3
2.6%
2.8%
Sent rude or nasty emails
2
1.7%
1.9%
Insulted someone on a
6
5.2%
5.6%
1
.9%
.9%
4
3.4%
3.7%
3
2.6%
2.8%
116
100.0%
108.4%
cyberbullying
Sent nasty texts messages (
threats and comments)
website
Insulted someone on Instant
Messenging ie MSN
,Messenger/AOL/Yahoo
Insulted someone in a chat
room
Other
Total
a. Dichotomy group tabulated at value 1. Note. Participant (N) may exceed (N=107) due to the ability
to give multiple responses to this question.
Traditional Vs Cyber-Bullying 80
Table 11A
Opinions on how a Victim of Bullying might feel?
Responses
Bully/Victim
s Feelings
Percent
Percent of Cases
Not affected
3
.6%
2.9%
Embarrased
72
13.3%
68.6%
Worried
70
12.9%
66.7%
Upset
85
15.7%
81.0%
Afraid/Scared
79
14.5%
75.2%
Angry
75
13.8%
71.4%
Depressed
80
14.7%
76.2%
Stressed
71
13.1%
67.6%
8
1.5%
7.6%
543
100.0%
517.1%
Other
Total
N
a. Dichotomy group tabulated at value 1. Note. Participant (N) may exceed (N=107)
due to the ability to give multiple responses to this question.
Traditional Vs Cyber-Bullying 81
Table 11B
Opinions on how a victim of cyber-bullying might feel?
Responses
N
Percent of Cases
Not affected
10
2.0%
9.6%
Embarrased
68
13.7%
65.4%
Worried
67
13.5%
64.4%
Upset
72
14.5%
69.2%
Afraid/Scared
63
12.7%
60.6%
Angry
70
14.1%
67.3%
Depressed
72
14.5%
69.2%
Stressed
70
14.1%
67.3%
6
1.2%
5.8%
498
100.0%
478.8%
Other
Total
Percent
a. Dichotomy group tabulated at value 1. Note. Participant (N) may exceed (N=107)
due to the ability to give multiple responses to this question.
Traditional Vs Cyber-Bullying 82
Table 12A
Opinions on how best to stop Bullying?
Male/Female
How to stop Bullying
Asking them to stop
male
Count
% of Total
Fighting back
Count
% of Total
Ignoring it
Count
% of Total
Keeping a record of the
bullying incidents
Telling someone
(parent/teacher/work
Count
% of Total
Count
% of Total
female
Total
7
18
25
6.5%
16.8%
23.4%
19
19
38
17.8%
17.8%
35.5%
12
20
32
11.2%
18.7%
29.9%
7
22
29
6.5%
20.6%
27.1%
27
52
79
25.2%
48.6%
73.8%
15
33
48
14.0%
30.8%
44.9%
19
27
46
17.8%
25.2%
43.0%
9
16
25
8.4%
15.0%
23.4%
2
1
3
1.9%
.9%
2.8%
12
24
36
11.2%
22.4%
33.6%
2
2
4
1.9%
1.9%
3.7%
45
62
107
42.1%
57.9%
100.0%
collegue)
Reporting to the police or
other authorities
Count
% of Total
Sticking up for myself without Count
fighting
Avoiding the bullies
% of Total
Count
% of Total
Staying away from school
Count
% of Total
Making new friends
Count
% of Total
Other
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 83
Table 12B
Opinions on how best to stop Cyber-Bullying?
Male/Female
male
Blocking messages/identities Count
% of Total
Reporting to the police or the Count
authorities
Asking them to stop
% of Total
Count
% of Total
Fighting it back
Count
% of Total
Ignoring it
Count
% of Total
Keeping a record of offensive Count
emails or texts
Telling someone
(teacher/parent/work
% of Total
Count
% of Total
female
Total
30
48
78
28.3%
45.3%
73.6%
18
35
53
17.0%
33.0%
50.0%
6
14
20
5.7%
13.2%
18.9%
7
3
10
6.6%
2.8%
9.4%
17
24
41
16.0%
22.6%
38.7%
17
37
54
16.0%
34.9%
50.9%
20
35
55
18.9%
33.0%
51.9%
16
35
51
15.1%
33.0%
48.1%
2
3
5
1.9%
2.8%
4.7%
44
62
106
41.5%
58.5%
100.0%
colegue)
Changing email address or
phone numbers
Other
Count
% of Total
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
Note. Missing value (N=1)
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 84
Table 12C
Opinions on how best to stop Bullying?
European/Asian?
How to stop Bullying
Asking them to stop
European
Count
% of Total
Fighting back
Count
% of Total
Ignoring it
Count
% of Total
Keeping a record of the
bullying incidents
Telling someone
(parent/teacher/work
Count
% of Total
Count
% of Total
Asian
Total
13
12
25
12.1%
11.2%
23.4%
26
12
38
24.3%
11.2%
35.5%
17
15
32
15.9%
14.0%
29.9%
22
7
29
20.6%
6.5%
27.1%
56
23
79
52.3%
21.5%
73.8%
31
17
48
29.0%
15.9%
44.9%
35
11
46
32.7%
10.3%
43.0%
15
10
25
14.0%
9.3%
23.4%
0
3
3
.0%
2.8%
2.8%
23
13
36
21.5%
12.1%
33.6%
3
1
4
2.8%
.9%
3.7%
70
37
107
65.4%
34.6%
100.0%
collegue)
Reporting to the police or
other authorities
Count
% of Total
Sticking up for myself without Count
fighting
Avoiding the bullies
% of Total
Count
% of Total
Staying away from school
Count
% of Total
Making new friends
Count
% of Total
Other
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 85
Table 12D
Opinions on how best to stop Cyber-Bullying?
European/Asian?
How to stop CyberBullying
European
Blocking messages/identities Count
% of Total
Reporting to the police or the Count
authorities
Asking them to stop
% of Total
Count
% of Total
Fighting it back
Count
% of Total
Ignoring it
Count
% of Total
Keeping a record of offensive Count
emails or texts
Telling someone
(teacher/parent/work
% of Total
Count
% of Total
Asian
Total
51
27
78
48.1%
25.5%
73.6%
33
20
53
31.1%
18.9%
50.0%
11
9
20
10.4%
8.5%
18.9%
4
6
10
3.8%
5.7%
9.4%
22
19
41
20.8%
17.9%
38.7%
42
12
54
39.6%
11.3%
50.9%
45
10
55
42.5%
9.4%
51.9%
31
20
51
29.2%
18.9%
48.1%
3
2
5
2.8%
1.9%
4.7%
69
37
106
65.1%
34.9%
100.0%
colegue)
Changing email address or
phone numbers
Other
Count
% of Total
Count
% of Total
Total
Count
% of Total
Percentages and totals are based on respondents.
Note. Missing value (N=1)
a. Dichotomy group tabulated at value 1.
Traditional Vs Cyber-Bullying 86
Table 13A
Male Opinions on whether Cyber-bullying is more harmful than Bullying?
Less harmful
Same
More harmful
Through nasty text
messages
9
24
11
Happy Slapping
(pictures and video
recorded on mobile
phones)
Prank or silent phone
calls
6
16
19
12
20
10
Through rude/nasty
emails
11
22
9
Insults on websites
9
17
17
Insults on instant
messaging
11
20
11
In chat rooms
17
16
9
Note. Participant (N) may exceed (N=107) due to the ability to give multiple responses to this
question.
Traditional Vs Cyber-Bullying 87
Table 13B
Female Opinions on whether Cyber-bullying is more harmful than Bullying?
Less harmful
Same
More harmful
Through nasty text
messages
10
34
15
Happy Slapping
(pictures and video
recorded on mobile
phones)
Prank or silent phone
calls
2
26
30
20
25
12
Through rude/nasty
emails
9
34
14
Insults on websites
2
26
31
Insults on instant
messaging
9
37
12
In chat rooms
13
29
16
Note. Participant (N) may exceed (N=107) due to the ability to give multiple responses to this
question.
Traditional Vs Cyber-Bullying 88
Table 13C
European Opinions on whether Cyber-bullying is more harmful than Bullying?
Less harmful
Same
More harmful
Through nasty text
messages
6
46
16
Happy Slapping
(pictures and video
recorded on mobile
phones)
Prank or silent phone
calls
5
26
33
17
34
13
Through rude/nasty
emails
10
39
15
Insults on websites
7
27
33
Insults on instant
messaging
11
39
15
In chat rooms
21
32
15
Note. Participant (N) may exceed (N=107) due to the ability to give multiple responses to this
question.
Traditional Vs Cyber-Bullying 89
Table 13D
Asian Opinions on whether Cyber-bullying is more harmful than Bullying?
Less harmful
Same
More harmful
Through nasty text
messages
13
12
10
Happy Slapping
(pictures and video
recorded on mobile
phones)
Prank or silent phone
calls
3
16
16
15
11
9
Through rude/nasty
emails
10
17
8
Insults on websites
4
16
15
Insults on instant
messaging
9
18
8
In chat rooms
9
13
13
Note. Participant (N) may exceed (N=107) due to the ability to give multiple responses to this
question.
Traditional Vs Cyber-Bullying 90